面向分布式机器学习的通信高效量子算法
Communication-efficient Quantum Algorithm for Distributed Machine Learning
AI总结：
针对两方分布式数据场景，提出通信高效的量子算法求解最小二乘拟合与 softmax 回归，并借助量子加速的分布式内积和 Hamming 距离估计降低随数据量增长的通信复杂度。
摘要：
远程检测需求的不断增长以及训练数据量的持续增加，使通信约束下的分布式机器学习成为一个关键问题。本工作提出一种通信高效的量子算法，在数据集分布于两方的场景下，解决最小二乘拟合和 softmax 回归这两个传统机器学习问题。我们的量子算法以 $O(\frac{\log_2(N)}ε)$ 的通信复杂度求得模型参数，其中 $N$ 是数据点数量，$ε$ 是参数误差的界。与完成相同输出任务的经典算法及其他量子算法相比，本算法在随数据量扩展的通信复杂度方面具有优势。该算法的构建模块，即对分布式内积和 Hamming 距离的量子加速估计，还可进一步应用于分布式机器学习中的各类任务，以加速通信。
英文摘要：
The growing demands of remote detection and increasing amount of training data make distributed machine learning under communication constraints a critical issue. This work provides a communication-efficient quantum algorithm that tackles two traditional machine learning problems, the least-square fitting and softmax regression problem, in the scenario where the data set is distributed across two parties. Our quantum algorithm finds the model parameters with a communication complexity of $O(\frac{\log_2(N)}ε)$, where $N$ is the number of data points and $ε$ is the bound on parameter errors. Compared to classical algorithms and other quantum algorithms that achieve the same output task, our algorithm provides a communication advantage in the scaling with the data volume. The building block of our algorithm, the quantum-accelerated estimation of distributed inner product and Hamming distance, could be further applied to various tasks in distributed machine learning to accelerate communication.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
